R Notebook
Libraries
RNAseq_Koichi <- read.csv("/media/alexis/DATA/Koichi_gene_expression_analyses_git/Koichi_gene_expression_analyses/DATA/RNAseq_parsed.csv", row.names = 1, header = T, check.names = F)
IDH_Koichi_TF_actitity_regulonlaml <- read.table("~/GitHub/Thesis_paper/Results/DGEA/TF_activities_tables/Koichi_TF_activities_Regulonlaml.tsv",
sep = "\t", check.names = F)
Clinical_patient_data <- read.table("~/GitHub/Thesis_paper/Datasets/Clinical_Koichi_data_isoform.tsv", sep = "\t", header = T)
Clinical_patient_data$Baseline_phenotype <- sapply(Clinical_patient_data$Best_response, function(resp){
switch(resp,
"CR" = "Responder",
"CRi" = "Responder",
"CRp" = "Intermediate_Responder",
"HI" = "Intermediate_Responder",
"MLFS" = "Intermediate_Responder",
"Not_assessed" = "Not_assessed",
"PD" = "Non_Responder",
"PR" = "Intermediate_Responder",
"SD" = "Non_Responder",
"NA" = "Control"
)
})
RNAseq_Koichi_Baseline <- RNAseq_Koichi[stringr::str_detect(colnames(RNAseq_Koichi), pattern="BL")]Phenos <- lapply(colnames(IDH_Koichi_TF_actitity_regulonlaml), function(sample){
tmp <- dplyr::filter(Clinical_patient_data, Baseline_RNAseq_data == sample)
list("Response" = tmp$Baseline_phenotype,
"Cluster" = tmp$Cluster,
"IDH" = tmp$IDH_isoform,
"IDH_1_2" = tmp$IDH_isoform %>% stringr::str_remove("_R172") %>% stringr::str_remove("_R140"))
}) %>% data.table::rbindlist()
rownames(Phenos) <- colnames(IDH_Koichi_TF_actitity_regulonlaml) Make_PCA_pheno <- function(data, pheno){
data <- data[!is.na(pheno)]
pheno <- pheno[!is.na(pheno)]
res.pca <- prcomp(t(data))
p <- fviz_pca_ind(res.pca, label="all", habillage=pheno, addEllipses=T, ellipse.level=0.95)
p <- p + ggtitle("PCA Transcripto")
p
}Make_PCA_pheno(RNAseq_Koichi_Baseline, Phenos$Response)
Make_PCA_pheno(IDH_Koichi_TF_actitity_regulonlaml, Phenos$Response)
Make_PCA_pheno(RNAseq_Koichi_Baseline, Phenos$IDH)
Make_PCA_pheno(IDH_Koichi_TF_actitity_regulonlaml, Phenos$IDH)
Make_heatmap <- function(DATA, Phenotype, method = "pearson",
title, annotation_color, kmeans_k = NA, cuttree = NA, corr=T) {
if(corr){
corr <- rcorr(as.matrix(DATA), type = method)$r
colnames(corr) <- colnames(DATA)
}else{
corr <- DATA
}
title <- paste0(title, " ", method)
heatmap <- pheatmap(corr,
color = colorRampPalette(brewer.pal(n = 9, name = "YlOrRd"))(100),
annotation_col = Phenotype,
annotation_colors = annotation_color,
legend = TRUE, scale = "none",
treeheight_row = 20,
main = title,
fontsize = 10,
cutree_cols = cuttree
)
return(heatmap)
}Annotation_color <- list(
Response = c(Non_Responder = "red", Responder = "green", Intermediate_Responder = "orange"),
Cluster = c(Cluster_R = "green", Cluster_NR = "red", No_clustered = "grey"),
IDH = c(IDH1 = "blue", IDH2_R140 = "pink", IDH2_R172 = "yellow"))
Datas <- list("RNAseq" = RNAseq_Koichi_Baseline,
"TF_activity" = IDH_Koichi_TF_actitity_regulonlaml)
names(Datas) %>% lapply(function(name_data){
data <- Datas[[name_data]]
Var_data <- rowVars(as.matrix(data))
names(Var_data) <- rownames(data)
pourcent_elem <- c(1, 5, 10, 50, 100)
method = c("pearson","spearman")
res <- lapply(method, function(meth){
plots_pearson <- lapply(pourcent_elem, function(percent){
nb_elem <- percent*nrow(data)%/%100
Top_var <- Var_data[order(Var_data, decreasing = T)] %>% .[1:nb_elem]
Top_Var_data <- data %>% .[rownames(.) %in% names(Top_var),]
heat <- Make_heatmap(Top_Var_data, Phenos, title = paste0(percent, "% of element"),
annotation_color = Annotation_color, method = meth)
pca_p <- Make_PCA_pheno(Top_Var_data, Phenos$Cluster)
list(heat, pca_p)
})
})
lm <- rbind(c(1,2),
c(3, 5),
c(4, 5),
c(6, 7),
c(8, 10),
c(9, 10))
gri <- grid.arrange(grobs = list(as.grob(res[[1]][[1]][[1]]), as.grob(res[[1]][[2]][[1]]),
as.grob(res[[1]][[3]][[1]]), as.grob(res[[1]][[4]][[1]]),
as.grob(res[[1]][[5]][[1]]),
as.grob(res[[2]][[1]][[1]]), as.grob(res[[2]][[2]][[1]]),
as.grob(res[[2]][[3]][[1]]), as.grob(res[[2]][[4]][[1]]),
as.grob(res[[2]][[5]][[1]])),
layout_matrix = lm)
ggsave(paste0("~/GitHub/Thesis_paper/Results/Transcriptomique/Heatmap_", name_data, ".png"), gri, bg = "white", width = 7600, height = 8400, units = "px")
})[[1]]
[1] "~/GitHub/Thesis_paper/Results/Transcriptomique/Heatmap_RNAseq.png"
[[2]]
[1] "~/GitHub/Thesis_paper/Results/Transcriptomique/Heatmap_TF_activity.png"
Differential gene expression analysis
mart <- useMart('ENSEMBL_MART_ENSEMBL')
mart <- useDataset('hsapiens_gene_ensembl', mart)
attrmart <- listAttributes(mart)
annotLookup <- getBM(
mart = mart,
attributes = c(
'hgnc_symbol',
'entrezgene_id',
'ensembl_gene_id'),
uniqueRows = TRUE)DEGs_IDH2s <- Differential_analysis(Focused_variable = Phenos$IDH, DATA = RNAseq_Koichi_Baseline)1 done
2 done
3 doneDEGs_IDH <- Differential_analysis(Focused_variable = Phenos$IDH_1_2, DATA = RNAseq_Koichi_Baseline) %>% .$`IDH1-IDH2` %>% merge(annotLookup, by.x = 0, by.y = "hgnc_symbol")1 doneVolcano_IDH1_IDH2 <- EnhancedVolcano(
toptable = DEGs_IDH,
lab = DEGs_IDH$ID,
x = "logFC",
y = "P.Value",
FCcutoff = 1.5,
pCutoff = 0.05,
title = "IDH1 vs IDH2",
subtitle = NA,
subtitleLabSize = 0, ylim = c(0, 5)
)
Volcano_IDH2_R140_IDH2_R172 <- EnhancedVolcano(
toptable = DEGs_IDH2s$`IDH2_R140-IDH2_R172`,
lab = DEGs_IDH2s$`IDH2_R140-IDH2_R172`$ID,
x = "logFC",
y = "P.Value",
FCcutoff = 1.5,
pCutoff = 0.05,
title = "IDH2 R140 vs IDH2 R172",
subtitle = NA,
subtitleLabSize = 0, ylim = c(0, 5)
)
Volcano_IDH1_IDH2 %>% ggsave(filename = "~/GitHub/Thesis_paper/Results/Transcriptomique/Volcano_plot_IDH1_vs_IDH2.png", bg = "white", width = 7600, height = 4200, units = "px")Avis dans grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
Unable to calculate text width/height (using zero)
Avis dans grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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Avis dans grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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Avis dans grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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Avis dans grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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Avis dans grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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Avis dans grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
Unable to calculate text width/height (using zero)Volcano_IDH2_R140_IDH2_R172
Gene set enrichment
GO
mart <- biomaRt::useMart(biomart = "ENSEMBL_MART_ENSEMBL",
dataset = "hsapiens_gene_ensembl")
genes <- getBM(filters = "hgnc_symbol",
attributes = c("hgnc_symbol","entrezgene_id"),
values = rownames(RNAseq_Koichi_Baseline),
mart = mart)
RNAseq_data_Entrezid <- merge(RNAseq_Koichi_Baseline, genes, by.x = 0, by.y = "hgnc_symbol", all.y = F, all.x = F)
genesets_list <- colnames(RNAseq_Koichi_Baseline) %>%
lapply(function(sample){
values <- RNAseq_data_Entrezid[sample] %>% c %>% .[[1]]
names(values) <- RNAseq_data_Entrezid$entrezgene_id
values[order(values, decreasing = T)]
})
names(genesets_list) <- colnames(RNAseq_Koichi_Baseline)divide_fun <- function(a,b){
n_samples <- length(b)
a/n_samples
}
Geneset_phenos <- Phenos$IDH_1_2 %>% unique %>% lapply(function(pheno){
samples <- colnames(RNAseq_Koichi_Baseline) %>% .[Phenos$IDH_1_2 == pheno]
genelist <- lapply(samples, function(sample){
genesets_list[[sample]]
})
purrr::reduce(genelist, `+`) %>% divide_fun(., samples)
})
names(Geneset_phenos) <- Phenos$IDH_1_2 %>% unique
ego <- lapply(names(Geneset_phenos), function(sample){
gseGO(geneList = Geneset_phenos[[sample]],
OrgDb = org.Hs.eg.db,
keyType = "ENTREZID",
ont = "all",
minGSSize = 20,
maxGSSize = 100,
pvalueCutoff = 0.01,
verbose = T)
})
names(ego) <- Phenos$IDH_1_2 %>% unique
Geneset_phenos_Response <- Phenos$Response %>% unique %>% lapply(function(pheno){
samples <- colnames(RNAseq_Koichi_Baseline) %>% .[Phenos$Response == pheno]
genelist <- lapply(samples, function(sample){
genesets_list[[sample]]
})
purrr::reduce(genelist, `+`) %>% divide_fun(., samples)
})
names(Geneset_phenos_Response) <- Phenos$Response %>% unique
ego_Response <- lapply(names(Geneset_phenos_Response), function(sample){
gseGO(geneList = Geneset_phenos_Response[[sample]],
OrgDb = org.Hs.eg.db,
keyType = "ENTREZID",
ont = "all",
minGSSize = 20,
maxGSSize = 100,
pvalueCutoff = 0.01,
verbose = T)
})
names(ego_Response) <- Phenos$Response %>% uniqueGeneset_IDH1_vs_IDH2 <- DEGs_IDH$logFC
names(Geneset_IDH1_vs_IDH2) <- DEGs_IDH$entrezgene_id
Geneset_IDH1_vs_IDH2 <- Geneset_IDH1_vs_IDH2[order(Geneset_IDH1_vs_IDH2, decreasing = T)]GSEA_IDH1_IDH2 <- gseGO(geneList = Geneset_IDH1_vs_IDH2,
OrgDb = org.Hs.eg.db,
keyType = "ENTREZID",
ont = "all",
minGSSize = 1,
maxGSSize = 1000,
pvalueCutoff = 0.1,
verbose = T) %>%
setReadable(OrgDb = org.Hs.eg.db, keyType = "ENTREZID")dotplot(GSEA_IDH1_IDH2, split=".sign") + facet_grid(.~.sign)
ggsave("~/GitHub/Thesis_paper/Results/Transcriptomique/GO_enrichment_IDH1_vs_IDH2.png", bg = "white", width = 7600, height = 4200, units = "px")
cnetplot(GSEA_IDH1_IDH2, foldChange=Geneset_IDH1_vs_IDH2, node_label = "all") Avis dans cnetplot.enrichResult(x, ...) :
Use 'color.params = list(foldChange = your_value)' instead of 'foldChange'.
The foldChange parameter will be removed in the next version.Scale for size is already present.
Adding another scale for size, which will replace the existing scale.Avis : ggrepel: 73 unlabeled data points (too many overlaps). Consider increasing max.overlapsggsave("~/GitHub/Thesis_paper/Results/Transcriptomique/GO_cnetplot_enrichment_IDH1_vs_IDH2.png", bg = "white", width = 7600, height = 4200, units = "px")
Go_similarities_IDH1_vs_IDH2 <- GO_similarity(GSEA_IDH1_IDH2@result$ID, ont = "BP")29/134 GO terms are removed.simplifyGO(Go_similarities_IDH1_vs_IDH2)Cluster 105 terms by 'binary_cut'... 14 clusters, used 0.7706757 secs.
'magick' package is suggested to install to give better rasterization.
Set `ht_opt$message = FALSE` to turn off this message.
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# Simplify_GO_IDH2 <- simplifyGO(Go_similarities$IDH2)
# Simplify_GO_IDH1 <- simplifyGO(Go_similarities$IDH1)lt <- list("IDH2" = ego$IDH2@result,
"IDH1" = ego$IDH1@result
)
simplifyGOFromMultipleLists_result <- simplifyGOFromMultipleLists(lt, padj_cutoff = 0.001, ont = "BP")
simplifyGOFromMultipleLists_result <- simplifyGOFromMultipleLists(lt, padj_cutoff = 0.001, ont = "CC")
simplifyGOFromMultipleLists_result <- simplifyGOFromMultipleLists(lt, padj_cutoff = 0.001, ont = "MF")GO
dotplot(ego$IDH1)
dotplot(ego$IDH2)KEGG
hsa00983 <- pathview(gene.data = Geneset_IDH1_vs_IDH2,
pathway.id = "hsa00983",
species = "hsa",
limit = list(gene=max(abs(Geneset_IDH1_vs_IDH2)), cpd=1))
Info: Downloading xml files for hsa00983, 1/1 pathways..
Info: Downloading png files for hsa00983, 1/1 pathways..
'select()' returned 1:1 mapping
between keys and columns
Info: Working in directory /home/alexis/GitHub/Thesis_paper/Scripts/Transcriptomique
Info: Writing image file hsa00983.pathview.pngcnetplot(KEGG_IDH, foldChange=Geneset_IDH1_vs_IDH2, node_label = "all")
Avis dans cnetplot.enrichResult(x, ...) :
Use 'color.params = list(foldChange = your_value)' instead of 'foldChange'.
The foldChange parameter will be removed in the next version.Scale for size is already present.
Adding another scale for size, which will replace the existing scale.Avis : ggrepel: 241 unlabeled data points (too many overlaps). Consider increasing max.overlaps
cnetplot(KEGG_IDH, foldChange=Geneset_IDH1_vs_IDH2, node_label = "all") Avis dans cnetplot.enrichResult(x, ...) :
Use 'color.params = list(foldChange = your_value)' instead of 'foldChange'.
The foldChange parameter will be removed in the next version.Scale for size is already present.
Adding another scale for size, which will replace the existing scale.Avis : ggrepel: 241 unlabeled data points (too many overlaps). Consider increasing max.overlapsggsave("~/GitHub/Thesis_paper/Results/Transcriptomique/KEGG_cnetplot_enrichment_IDH1_vs_IDH2.png", bg = "white", width = 7600, height = 4200, units = "px")
IDH_MKEGG <- gseMKEGG(geneList = Geneset_IDH1_vs_IDH2,
organism = 'hsa',
pvalueCutoff = 1) %>%
setReadable(OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
Reading KEGG annotation online: "https://rest.kegg.jp/link/hsa/module"...
Reading KEGG annotation online: "https://rest.kegg.jp/list/module"...
preparing geneSet collections...
GSEA analysis...
Avis dans preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (19.46% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Avis dans preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are duplicate gene names, fgsea may produce unexpected results.
leading edge analysis...
done...dotplot(IDH_MKEGG, split=".sign") + facet_grid(.~.sign)
ggsave("~/GitHub/Thesis_paper/Results/Transcriptomique/MKEGG_enrichment_IDH1_vs_IDH2.png", bg = "white", width = 7600, height = 4200, units = "px")
cnetplot(IDH_MKEGG, foldChange=Geneset_IDH1_vs_IDH2, node_label = "all") Avis dans cnetplot.enrichResult(x, ...) :
Use 'color.params = list(foldChange = your_value)' instead of 'foldChange'.
The foldChange parameter will be removed in the next version.Scale for size is already present.
Adding another scale for size, which will replace the existing scale.ggsave("~/GitHub/Thesis_paper/Results/Transcriptomique/MKEGG_cnetplot_enrichment_IDH1_vs_IDH2.png", bg = "white", width = 7600, height = 4200, units = "px")
Wikipathway
IDH_WIKI <- gseWP(geneList = Geneset_IDH1_vs_IDH2,
organism = 'Homo sapiens',
pvalueCutoff = 1) %>%
setReadable(OrgDb = org.Hs.eg.db, keyType = "ENTREZID")preparing geneSet collections...
GSEA analysis...
Avis dans preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are ties in the preranked stats (19.46% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
Avis dans preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, :
There are duplicate gene names, fgsea may produce unexpected results.
leading edge analysis...
done...cnetplot(IDH_WIKI, foldChange=Geneset_IDH1_vs_IDH2, node_label = "all")
Avis dans cnetplot.enrichResult(x, ...) :
Use 'color.params = list(foldChange = your_value)' instead of 'foldChange'.
The foldChange parameter will be removed in the next version.Scale for size is already present.
Adding another scale for size, which will replace the existing scale.Avis : ggrepel: 3 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Overrepresentation
IDH1_vs_IDH2_UP <- DEGs_IDH %>% dplyr::filter(logFC > 1.5 & P.Value < 0.05) %>% .$ID %>% unique
IDH1_vs_IDH2_DOWN <- DEGs_IDH %>% dplyr::filter(logFC < -1.5 & P.Value < 0.05) %>% .$ID %>% uniqueGO
IDH1_vs_IDH2_UP_GO <- enrichGO(IDH1_vs_IDH2_UP, OrgDb = org.Hs.eg.db, universe = DEGs_IDH$ID, keyType = "SYMBOL", pvalueCutoff = 0.05)
IDH1_vs_IDH2_DOWN_GO <- enrichGO(IDH1_vs_IDH2_DOWN, OrgDb = org.Hs.eg.db, universe = DEGs_IDH$ID, keyType = "SYMBOL", pvalueCutoff = 0.05)dotplot(IDH1_vs_IDH2_UP_GO, split=".sign") + facet_grid(.~.sign) Erreur dans `[.data.frame`(res, , split) :
colonnes non définies sélectionnéesKEGG
Overrepresented_IDH1 <- dplyr::filter(annotLookup, hgnc_symbol %in% IDH1_vs_IDH2_UP) %>%
.$entrezgene_id %>% unique
Overrepresented_IDH2 <- dplyr::filter(annotLookup, hgnc_symbol %in% IDH1_vs_IDH2_DOWN) %>%
.$entrezgene_id %>% unique
universe_entrez <- dplyr::filter(annotLookup, hgnc_symbol %in% rownames(RNAseq_Koichi_Baseline)) %>%
.$entrezgene_id %>% unique %>% as.character()IDH1_vs_IDH2_UP_KEGG <- enrichKEGG(Overrepresented_IDH1, organism = "hsa", universe = universe_entrez) %>%
setReadable(OrgDb = org.Hs.eg.db, keyType = "ENTREZID")Reading KEGG annotation online: "https://rest.kegg.jp/link/hsa/pathway"...
Reading KEGG annotation online: "https://rest.kegg.jp/list/pathway/hsa"...IDH1_vs_IDH2_DOWN_KEGG <- enrichKEGG(Overrepresented_IDH2, organism = "hsa", universe = universe_entrez) %>%
setReadable(OrgDb = org.Hs.eg.db, keyType = "ENTREZID")dotplot(IDH1_vs_IDH2_UP_KEGG)
dotplot(IDH1_vs_IDH2_DOWN_KEGG) Avis dans rep(yes, length.out = len) :
'x' is NULL so the result will be NULL
Erreur dans ans[ypos] <- rep(yes, length.out = len)[ypos] :
l'argument de remplacement est de longueur nulle
Wikipathway
IDH1_vs_IDH2_WIKIPATH_over_up <- enrichWP(Overrepresented_IDH1, organism = "Homo sapiens")
IDH1_vs_IDH2_WIKIPATH_over_down <- enrichWP(Overrepresented_IDH2, organism = "Homo sapiens")
write.table(GSEA_IDH1_IDH2@result, "~/GitHub/Thesis_paper/Results/Transcriptomique/GO_enrichment_IDH1_vs_IDH2.tsv", sep = "\t")
write.table(KEGG_IDH@result, "~/GitHub/Thesis_paper/Results/Transcriptomique/KEGG_enrichment_IDH1_vs_IDH2.tsv", sep = "\t")
write.table(IDH_MKEGG@result, "~/GitHub/Thesis_paper/Results/Transcriptomique/MKEGG_enrichment_IDH1_vs_IDH2.tsv", sep = "\t")
write.table(IDH_WIKI@result, "~/GitHub/Thesis_paper/Results/Transcriptomique/WIKI_enrichment_IDH1_vs_IDH2.tsv", sep = "\t")
---
title: "R Notebook"
output: html_notebook
---

# Libraries

```{r, error=F, warning=F, echo=FALSE, include=F}
library(dplyr)
library(matrixTests)
library(factoextra)
library(Hmisc)
library(matrixStats)
library(RColorBrewer)
library(pheatmap)
library(data.table)
library(parallel)
library(ggpubr)
library("grid")
library("gridExtra")
library(ggplotify)
library(limma)
library(EnhancedVolcano)
library(biomaRt)
library(clusterProfiler)
library(org.Hs.eg.db)
library(simplifyEnrichment)
library(pathview)


cores2use <- detectCores() -2


source("~/Core_scripts/core_functions.R")
```


```{r}
RNAseq_Koichi <- read.csv("/media/alexis/DATA/Koichi_gene_expression_analyses_git/Koichi_gene_expression_analyses/DATA/RNAseq_parsed.csv", row.names = 1, header = T, check.names = F)

IDH_Koichi_TF_actitity_regulonlaml <- read.table("~/GitHub/Thesis_paper/Results/DGEA/TF_activities_tables/Koichi_TF_activities_Regulonlaml.tsv", 
                                                sep = "\t", check.names = F)
Clinical_patient_data <- read.table("~/GitHub/Thesis_paper/Datasets/Clinical_Koichi_data_isoform.tsv", sep = "\t", header = T)

Clinical_patient_data$Baseline_phenotype <- sapply(Clinical_patient_data$Best_response, function(resp){
  switch(resp,
         "CR" = "Responder",
         "CRi" = "Responder",
         "CRp" = "Intermediate_Responder",
         "HI" = "Intermediate_Responder",
         "MLFS" = "Intermediate_Responder",
         "Not_assessed" = "Not_assessed",
         "PD" = "Non_Responder",
         "PR" = "Intermediate_Responder",
         "SD" = "Non_Responder",
         "NA" = "Control"
         )
})

RNAseq_Koichi_Baseline <- RNAseq_Koichi[stringr::str_detect(colnames(RNAseq_Koichi), pattern="BL")]
```

```{r}
Phenos <- lapply(colnames(IDH_Koichi_TF_actitity_regulonlaml), function(sample){
  tmp <- dplyr::filter(Clinical_patient_data, Baseline_RNAseq_data == sample)
  list("Response" = tmp$Baseline_phenotype,
       "Cluster" = tmp$Cluster,
       "IDH" = tmp$IDH_isoform,
       "IDH_1_2" = tmp$IDH_isoform %>% stringr::str_remove("_R172") %>% stringr::str_remove("_R140"))
}) %>% data.table::rbindlist()
rownames(Phenos) <- colnames(IDH_Koichi_TF_actitity_regulonlaml) 
```


```{r}
Make_PCA_pheno <- function(data, pheno){
  data <- data[!is.na(pheno)]
  pheno <- pheno[!is.na(pheno)]
  res.pca <- prcomp(t(data))
  p <- fviz_pca_ind(res.pca, label="all", habillage=pheno, addEllipses=T, ellipse.level=0.95)
  p <- p + ggtitle("PCA Transcripto")
  p
}
```

```{r}
Make_PCA_pheno(RNAseq_Koichi_Baseline, Phenos$Response)
Make_PCA_pheno(IDH_Koichi_TF_actitity_regulonlaml, Phenos$Response)
Make_PCA_pheno(RNAseq_Koichi_Baseline, Phenos$IDH)
Make_PCA_pheno(IDH_Koichi_TF_actitity_regulonlaml, Phenos$IDH)
```

```{r}
Make_heatmap <- function(DATA, Phenotype, method = "pearson", 
                         title, annotation_color, kmeans_k = NA, cuttree = NA, corr=T) {
  if(corr){
    corr <- rcorr(as.matrix(DATA), type = method)$r
    colnames(corr) <- colnames(DATA)
  }else{
    corr <- DATA
  }
  title <- paste0(title, " ", method)
  heatmap <- pheatmap(corr, 
                      color = colorRampPalette(brewer.pal(n = 9, name = "YlOrRd"))(100),
                      annotation_col = Phenotype,
                      annotation_colors = annotation_color,
                      legend = TRUE, scale = "none",
                      treeheight_row = 20,
                      main = title, 
                      fontsize = 10,
                      cutree_cols = cuttree
  )
  return(heatmap)
}
```

```{r}
Annotation_color <-  list(
    Response = c(Non_Responder = "red", Responder = "green", Intermediate_Responder = "orange"),
    Cluster = c(Cluster_R = "green", Cluster_NR = "red", No_clustered = "grey"),
    IDH = c(IDH1 = "blue", IDH2_R140 = "pink", IDH2_R172 = "yellow"))

Datas <- list("RNAseq" = RNAseq_Koichi_Baseline,
              "TF_activity" = IDH_Koichi_TF_actitity_regulonlaml)

names(Datas) %>% lapply(function(name_data){
  data <- Datas[[name_data]]
  Var_data <- rowVars(as.matrix(data))
  names(Var_data) <- rownames(data)
  pourcent_elem <- c(1, 5, 10, 50, 100)
  method = c("pearson","spearman")
  res <- lapply(method, function(meth){
    plots_pearson <- lapply(pourcent_elem, function(percent){
      nb_elem <- percent*nrow(data)%/%100 
      Top_var <- Var_data[order(Var_data, decreasing = T)] %>% .[1:nb_elem]
      Top_Var_data <- data %>% .[rownames(.) %in% names(Top_var),]
      heat <- Make_heatmap(Top_Var_data, Phenos, title = paste0(percent, "% of element"), 
                           annotation_color = Annotation_color, method = meth)
      pca_p <- Make_PCA_pheno(Top_Var_data, Phenos$Cluster)
      list(heat, pca_p)
    })
  })
  
  lm <- rbind(c(1,2),
           c(3, 5),
           c(4, 5),
           c(6, 7),
           c(8, 10),
           c(9, 10))
  gri <- grid.arrange(grobs = list(as.grob(res[[1]][[1]][[1]]), as.grob(res[[1]][[2]][[1]]),
                            as.grob(res[[1]][[3]][[1]]), as.grob(res[[1]][[4]][[1]]),
                            as.grob(res[[1]][[5]][[1]]),
                            as.grob(res[[2]][[1]][[1]]), as.grob(res[[2]][[2]][[1]]),
                            as.grob(res[[2]][[3]][[1]]), as.grob(res[[2]][[4]][[1]]),
                            as.grob(res[[2]][[5]][[1]])),
               layout_matrix = lm)
  ggsave(paste0("~/GitHub/Thesis_paper/Results/Transcriptomique/Heatmap_", name_data, ".png"), gri, bg = "white", width = 7600, height = 8400, units = "px")

})


```

# Differential gene expression analysis

```{r}
mart <- useMart('ENSEMBL_MART_ENSEMBL')
mart <- useDataset('hsapiens_gene_ensembl', mart)

attrmart <- listAttributes(mart)

annotLookup <- getBM(
  mart = mart,
  attributes = c(
    'hgnc_symbol',
    'entrezgene_id', 
    'ensembl_gene_id'),
  uniqueRows = TRUE)
```


```{r}
DEGs_IDH2s <- Differential_analysis(Focused_variable = Phenos$IDH, DATA = RNAseq_Koichi_Baseline)
DEGs_IDH <- Differential_analysis(Focused_variable = Phenos$IDH_1_2, DATA = RNAseq_Koichi_Baseline) %>% .$`IDH1-IDH2` %>% merge(annotLookup, by.x = 0, by.y = "hgnc_symbol")
```

```{r}
Volcano_IDH1_IDH2 <- EnhancedVolcano(
      toptable = DEGs_IDH,
      lab = DEGs_IDH$ID,
      x = "logFC",
      y = "P.Value",
      FCcutoff = 1.5,
      pCutoff = 0.05,
      title = "IDH1 vs IDH2",
      subtitle = NA,
      subtitleLabSize = 0, ylim = c(0, 5)
  )
Volcano_IDH2_R140_IDH2_R172 <- EnhancedVolcano(
      toptable = DEGs_IDH2s$`IDH2_R140-IDH2_R172`,
      lab = DEGs_IDH2s$`IDH2_R140-IDH2_R172`$ID,
      x = "logFC",
      y = "P.Value",
      FCcutoff = 1.5,
      pCutoff = 0.05,
      title = "IDH2 R140 vs IDH2 R172",
      subtitle = NA,
      subtitleLabSize = 0, ylim = c(0, 5)
  )

Volcano_IDH1_IDH2 %>% ggsave(filename = "~/GitHub/Thesis_paper/Results/Transcriptomique/Volcano_plot_IDH1_vs_IDH2.png", bg = "white", width = 7600, height = 4200, units = "px")
Volcano_IDH2_R140_IDH2_R172
```

# Gene set enrichment

## GO

```{r, eval = F}
mart <- biomaRt::useMart(biomart = "ENSEMBL_MART_ENSEMBL",
                         dataset = "hsapiens_gene_ensembl")

genes <- getBM(filters = "hgnc_symbol",
               attributes = c("hgnc_symbol","entrezgene_id"),
               values = rownames(RNAseq_Koichi_Baseline), 
               mart = mart) 

RNAseq_data_Entrezid <- merge(RNAseq_Koichi_Baseline, genes, by.x = 0, by.y = "hgnc_symbol", all.y = F, all.x = F)

genesets_list <- colnames(RNAseq_Koichi_Baseline) %>% 
  lapply(function(sample){
    values <- RNAseq_data_Entrezid[sample] %>% c %>% .[[1]]
    names(values) <- RNAseq_data_Entrezid$entrezgene_id
    values[order(values, decreasing = T)]
  })
names(genesets_list) <- colnames(RNAseq_Koichi_Baseline)
```

```{r, eval = F}
divide_fun <- function(a,b){
  n_samples <- length(b)
  a/n_samples
}

Geneset_phenos <- Phenos$IDH_1_2 %>% unique %>% lapply(function(pheno){
  samples <- colnames(RNAseq_Koichi_Baseline) %>% .[Phenos$IDH_1_2 == pheno]
  genelist <- lapply(samples, function(sample){
    genesets_list[[sample]]
  }) 
  purrr::reduce(genelist, `+`) %>% divide_fun(., samples)
})
names(Geneset_phenos) <- Phenos$IDH_1_2 %>% unique
  
ego <- lapply(names(Geneset_phenos), function(sample){
  gseGO(geneList     = Geneset_phenos[[sample]],
              OrgDb        = org.Hs.eg.db, 
              keyType = "ENTREZID",
              ont          = "all",
              minGSSize    = 20,
              maxGSSize    = 100,
              pvalueCutoff = 0.01,
              verbose      = T)
})
names(ego) <-  Phenos$IDH_1_2 %>% unique

Geneset_phenos_Response <- Phenos$Response %>% unique %>% lapply(function(pheno){
  samples <- colnames(RNAseq_Koichi_Baseline) %>% .[Phenos$Response == pheno]
  genelist <- lapply(samples, function(sample){
    genesets_list[[sample]]
  }) 
  purrr::reduce(genelist, `+`) %>% divide_fun(., samples)
})
names(Geneset_phenos_Response) <- Phenos$Response %>% unique

ego_Response <- lapply(names(Geneset_phenos_Response), function(sample){
  gseGO(geneList     = Geneset_phenos_Response[[sample]],
              OrgDb        = org.Hs.eg.db, 
              keyType = "ENTREZID",
              ont          = "all",
              minGSSize    = 20,
              maxGSSize    = 100,
              pvalueCutoff = 0.01,
              verbose      = T)
})
names(ego_Response) <-  Phenos$Response %>% unique
```

```{r}
Geneset_IDH1_vs_IDH2 <- DEGs_IDH$logFC
names(Geneset_IDH1_vs_IDH2) <- DEGs_IDH$entrezgene_id
Geneset_IDH1_vs_IDH2 <- Geneset_IDH1_vs_IDH2[order(Geneset_IDH1_vs_IDH2, decreasing = T)]
```

```{r}
GSEA_IDH1_IDH2 <- gseGO(geneList     = Geneset_IDH1_vs_IDH2,
              OrgDb        = org.Hs.eg.db, 
              keyType = "ENTREZID",
              ont          = "all",
              minGSSize    = 1,
              maxGSSize    = 1000,
              pvalueCutoff = 0.1,
              verbose      = T) %>%
  setReadable(OrgDb = org.Hs.eg.db, keyType = "ENTREZID")

```

```{r}
dotplot(GSEA_IDH1_IDH2, split=".sign") + facet_grid(.~.sign)

ggsave("~/GitHub/Thesis_paper/Results/Transcriptomique/GO_enrichment_IDH1_vs_IDH2.png", bg = "white", width = 7600, height = 4200, units = "px")

cnetplot(GSEA_IDH1_IDH2, foldChange=Geneset_IDH1_vs_IDH2, node_label = "all") 

ggsave("~/GitHub/Thesis_paper/Results/Transcriptomique/GO_cnetplot_enrichment_IDH1_vs_IDH2.png", bg = "white", width = 7600, height = 4200, units = "px")
```



```{r}
Go_similarities_IDH1_vs_IDH2 <- GO_similarity(GSEA_IDH1_IDH2@result$ID, ont = "BP")
```

```{r}
simplifyGO(Go_similarities_IDH1_vs_IDH2)
```


```{r, eval = F}
# Simplify_GO_IDH2 <- simplifyGO(Go_similarities$IDH2)
# Simplify_GO_IDH1 <- simplifyGO(Go_similarities$IDH1)
```

```{r, eval = F}
lt <- list("IDH2" = ego$IDH2@result,
           "IDH1" = ego$IDH1@result
           )
simplifyGOFromMultipleLists_result <- simplifyGOFromMultipleLists(lt, padj_cutoff = 0.001, ont = "BP")
simplifyGOFromMultipleLists_result <- simplifyGOFromMultipleLists(lt, padj_cutoff = 0.001, ont = "CC")
simplifyGOFromMultipleLists_result <- simplifyGOFromMultipleLists(lt, padj_cutoff = 0.001, ont = "MF")
```

## GO

```{r, eval = F}
dotplot(ego$IDH1)
dotplot(ego$IDH2)
```

## KEGG

```{r}
KEGG_IDH <- gseKEGG(Geneset_IDH1_vs_IDH2, minGSSize = 2, pvalueCutoff = 0.1, pAdjustMethod = "none", verbose = F, organism = 'hsa', keyType = 'ncbi-geneid') %>%
  setReadable(OrgDb = org.Hs.eg.db, keyType = "ENTREZID")

hsa00982 <- pathview(gene.data  = Geneset_IDH1_vs_IDH2,
                     pathway.id = "hsa00982",
                     species    = "hsa",
                     limit      = list(gene=max(abs(Geneset_IDH1_vs_IDH2)), cpd=1))

hsa04064 <- pathview(gene.data  = Geneset_IDH1_vs_IDH2,
                     pathway.id = "hsa04064",
                     species    = "hsa",
                     limit      = list(gene=max(abs(Geneset_IDH1_vs_IDH2)), cpd=1))

hsa00260 <- pathview(gene.data  = Geneset_IDH1_vs_IDH2,
                     pathway.id = "hsa00260",
                     species    = "hsa",
                     limit      = list(gene=max(abs(Geneset_IDH1_vs_IDH2)), cpd=1))

hsa00983 <- pathview(gene.data  = Geneset_IDH1_vs_IDH2,
                     pathway.id = "hsa00983",
                     species    = "hsa",
                     limit      = list(gene=max(abs(Geneset_IDH1_vs_IDH2)), cpd=1))


```

```{r}
dotplot(KEGG_IDH, split=".sign") + facet_grid(.~.sign, ) 

ggsave("~/GitHub/Thesis_paper/Results/Transcriptomique/KEGG_enrichment_IDH1_vs_IDH2.png", bg = "white", width = 7600, height = 4200, units = "px")

cnetplot(KEGG_IDH, foldChange=Geneset_IDH1_vs_IDH2, node_label = "all") 

ggsave("~/GitHub/Thesis_paper/Results/Transcriptomique/KEGG_cnetplot_enrichment_IDH1_vs_IDH2.png", bg = "white", width = 7600, height = 4200, units = "px")
```


```{r}
IDH_MKEGG <- gseMKEGG(geneList = Geneset_IDH1_vs_IDH2,
                 organism = 'hsa',
                 pvalueCutoff = 1) %>%
  setReadable(OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
```

```{r}
dotplot(IDH_MKEGG, split=".sign") + facet_grid(.~.sign)

ggsave("~/GitHub/Thesis_paper/Results/Transcriptomique/MKEGG_enrichment_IDH1_vs_IDH2.png", bg = "white", width = 7600, height = 4200, units = "px")

cnetplot(IDH_MKEGG, foldChange=Geneset_IDH1_vs_IDH2, node_label = "all") 

ggsave("~/GitHub/Thesis_paper/Results/Transcriptomique/MKEGG_cnetplot_enrichment_IDH1_vs_IDH2.png", bg = "white", width = 7600, height = 4200, units = "px")
```

## Wikipathway

```{r}
IDH_WIKI <- gseWP(geneList = Geneset_IDH1_vs_IDH2,
                 organism = 'Homo sapiens',
                 pvalueCutoff = 1)  %>%
  setReadable(OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
```

```{r}
dotplot(IDH_WIKI, split=".sign") + facet_grid(.~.sign) 

ggsave("~/GitHub/Thesis_paper/Results/Transcriptomique/WIKI_enrichment_IDH1_vs_IDH2.png", bg = "white", width = 7600, height = 4200, units = "px")

cnetplot(IDH_WIKI, foldChange=Geneset_IDH1_vs_IDH2, node_label = "all") 

ggsave("~/GitHub/Thesis_paper/Results/Transcriptomique/Wiki_cnetplot_enrichment_IDH1_vs_IDH2.png", bg = "white", width = 7600, height = 4200, units = "px")
```


# Overrepresentation

```{r}
IDH1_vs_IDH2_UP <- DEGs_IDH %>% dplyr::filter(logFC > 1.5 & P.Value < 0.05) %>% .$ID %>% unique
IDH1_vs_IDH2_DOWN <- DEGs_IDH %>% dplyr::filter(logFC < -1.5 & P.Value < 0.05) %>% .$ID %>% unique
```

## GO

```{r}
IDH1_vs_IDH2_UP_GO <- enrichGO(IDH1_vs_IDH2_UP, OrgDb = org.Hs.eg.db, universe = DEGs_IDH$ID, keyType = "SYMBOL", pvalueCutoff = 0.05)
IDH1_vs_IDH2_DOWN_GO <- enrichGO(IDH1_vs_IDH2_DOWN, OrgDb = org.Hs.eg.db, universe = DEGs_IDH$ID, keyType = "SYMBOL", pvalueCutoff = 0.05)
```


```{r}
dotplot(IDH1_vs_IDH2_UP_GO, split=".sign") + facet_grid(.~.sign) 
dotplot(IDH1_vs_IDH2_DOWN_GO, split=".sign") + facet_grid(.~.sign) 
```

## KEGG

```{r}
Overrepresented_IDH1 <- dplyr::filter(annotLookup, hgnc_symbol %in% IDH1_vs_IDH2_UP) %>% 
  .$entrezgene_id %>% unique
Overrepresented_IDH2 <- dplyr::filter(annotLookup, hgnc_symbol %in% IDH1_vs_IDH2_DOWN) %>% 
  .$entrezgene_id %>% unique
universe_entrez <- dplyr::filter(annotLookup, hgnc_symbol %in% rownames(RNAseq_Koichi_Baseline)) %>% 
  .$entrezgene_id %>% unique %>% as.character()
```


```{r}
IDH1_vs_IDH2_UP_KEGG <- enrichKEGG(Overrepresented_IDH1, organism = "hsa", universe = universe_entrez) %>%
  setReadable(OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
IDH1_vs_IDH2_DOWN_KEGG <- enrichKEGG(Overrepresented_IDH2, organism = "hsa", universe = universe_entrez) %>%
  setReadable(OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
```

```{r}
dotplot(IDH1_vs_IDH2_UP_KEGG)
dotplot(IDH1_vs_IDH2_DOWN_KEGG) 
```

## Wikipathway

```{r}
IDH1_vs_IDH2_WIKIPATH_over_up <- enrichWP(Overrepresented_IDH1, organism = "Homo sapiens")
IDH1_vs_IDH2_WIKIPATH_over_down <- enrichWP(Overrepresented_IDH2, organism = "Homo sapiens")
```

```{r}
dotplot(IDH1_vs_IDH2_WIKIPATH_over_up)
dotplot(IDH1_vs_IDH2_WIKIPATH_over_down)
```


```{r}
write.table(GSEA_IDH1_IDH2@result, "~/GitHub/Thesis_paper/Results/Transcriptomique/GO_enrichment_IDH1_vs_IDH2.tsv", sep = "\t")
write.table(KEGG_IDH@result, "~/GitHub/Thesis_paper/Results/Transcriptomique/KEGG_enrichment_IDH1_vs_IDH2.tsv", sep = "\t")
write.table(IDH_MKEGG@result, "~/GitHub/Thesis_paper/Results/Transcriptomique/MKEGG_enrichment_IDH1_vs_IDH2.tsv", sep = "\t")
write.table(IDH_WIKI@result, "~/GitHub/Thesis_paper/Results/Transcriptomique/WIKI_enrichment_IDH1_vs_IDH2.tsv", sep = "\t")
```







